Maritime pine (Pinus pinasterAit.) is a widely distributed conifer species in Southwestern Europe and one of the most advanced models for conifer research. In the current work, comprehensive characterization of the maritime pine transcriptome was performed using a combination of two different next‐generation sequencing platforms, 454 and Illumina. De novo assembly of the transcriptome provided a catalogue of 26 020 unique transcripts in maritime pine trees and a collection of 9641 full‐length cDNAs. Quality of the transcriptome assembly was validated by RT‐PCR amplification of selected transcripts for structural and regulatory genes. Transcription factors and enzyme‐encoding transcripts were annotated. Furthermore, the available sequencing data permitted the identification of polymorphisms and the establishment of robust single nucleotide polymorphism (SNP) and simple‐sequence repeat (SSR) databases for genotyping applications and integration of translational genomics in maritime pine breeding programmes. All our data are freely available at SustainpineDB, the P. pinaster expressional database. Results reported here on the maritime pine transcriptome represent a valuable resource for future basic and applied studies on this ecological and economically important pine species.

In spite of the biological and economic importance of plants, relatively few plant species have been sequenced. Only the genome sequence of plants with relatively small genomes, most of them angiosperms, in particular eudicots, has been determined. The arrival of next-generation sequencing technologies has allowed the rapid and efficient development of new genomic resources for non-model or orphan plant species. But the sequencing pace of plants is far from that of animals and microorganisms. This review focuses on the typical challenges of plant genomes that can explain why plant genomics is less developed than animal genomics. Explanations about the impact of some confounding factors emerging from the nature of plant genomes are given. As a result of these challenges and confounding factors, the correct assembly and annotation of plant genomes is hindered, genome drafts are produced, and advances in plant genomics are delayed.

Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin–chromatin and chromatin–protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.

Plant reproductive transcriptomes have been analysed in different species due to the agronomical and biotechnological importance of plant reproduction. Here we presented an olive tree reproductive transcriptome database with samples from pollen and pistil at different developmental stages, and leaf and root as control vegetative tissues (http://reprolive.eez.csic.es). It was developed from 2,077,309 raw reads and 1,549 Sanger sequences. Using a pre-defined workflow based on open-source tools, sequences were pre-processed, assembled, mapped and annotated with expression data, descriptions, GO terms, InterPro signatures, EC numbers, KEGG pathways, ORFs, and SSRs. Tentative transcripts were also annotated with the corresponding orthologues in Arabidopsis thaliana from TAIR and RefSeq databases to enable Linked Data integration. It results in a reproductive transcriptome comprising 72,846 contigs with average length of 686 bp, of which 63,965 (87.8%) included at least one functional annotation, and 55,356 (75.9%) had an orthologue. A minimum of 23,568 different tentative transcripts was identified and 5,835 of them contain a complete ORF. The representative reproductive transcriptome can be reduced to 28,972 tentative transcripts for further gene expression studies. Partial transcriptomes from pollen, pistil and vegetative tissues as control were also constructed. ReprOlive provides free access and download capability to these results. Retrieval mechanisms for sequences and transcript annotations are provided. Graphical localisation of annotated enzymes into KEGG pathways is also possible. Finally, ReprOlive has included a semantic conceptualisation by means of a Resource Description Framework (RDF) allowing a Linked Data search for extracting the most updated information related to enzymes, interactions, allergens, structures and reactive oxygen species.

Faba bean is an important food crop worldwide. However, progress in faba bean genomics lags far behind that of model systems due to limited availability of genetic and genomic information. Using the Illumina platform the faba bean transcriptome from leaves of two lines (29H and Vf136) subjected to Ascochyta fabae infection have been characterized. De novo transcriptome assembly provided a total of 39,185 different transcripts that were functionally annotated, and among these, 13,266 were assigned to gene ontology against Arabidopsis. Quality of the assembly was validated by RT-qPCR amplification of selected transcripts differentially expressed. Comparison of faba bean transcripts with those of better-characterized plant genomes such as Arabidopsis thaliana, Medicago truncatula and Cicer arietinum revealed a sequence similarity of 68.3%, 72.8% and 81.27%, respectively. Moreover, 39,060 single nucleotide polymorphism (SNP) and 3,669 InDels were identified for genotyping applications. Mapping of the sequence reads generated onto the assembled transcripts showed that 393 and 457 transcripts were overexpressed in the resistant (29H) and susceptible genotype (Vf136), respectively. Transcripts involved in plant-pathogen interactions such as leucine rich proteins (LRR) or plant growth regulators involved in plant adaptation to abiotic and biotic stresses were found to be differently expressed in the resistant line. The results reported here represent the most comprehensive transcript database developed so far in faba bean, providing valuable information that could be used to gain insight into the pathways involved in the resistance mechanism against A. fabae and to identify potential resistance genes to be further used in marker assisted selection.

Differential gene expression based on RNA-seq is widely used. Bioinformatics skills are required since no algorithm is appropriate for all experimental designs. Moreover, when working with organisms without reference genome, functional analysis is less than straightforward in most situations. DEgenes Hunter, an attempt to automate the process, is based on two independent scripts, one for differential expression and one for functional interpretation. Based on replicates, the R script decides which of the edgeR, DEseq2, NOISeq and limma algorithms are appropriate. It performs quality control calculations and provides the prevalent, most reliable, set of differentially expressed genes, and lists all other possible candidates for further functional interpretation. It also provides a combined P-value that allows differentially expressed genes ranking. It has been tested with synthetic and real-world datasets, showing in both cases ease of use and reliable results. With real data, DEgenes Hunter offers straightforward functional interpretation.